TriViews: A general framework to use 3D depth data effectively for action recognition

By Wenbin Chen, Guo-qing Guo. 2015

26 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jun Liu
2. Qiuhong Ke
3. Hossein Rahmani
4. Zehua Sun
5. Yi-Xiang Zhang
6. Hongbo Zhang
7. Lijie Yang
8. Qing Lei

In Computer Vision and Pattern Recognition

1. Qiuhong Ke
2. Jun Liu
3. Hossein Rahmani
4. Zehua Sun
5. Yi-Xiang Zhang
6. Hongbo Zhang
7. Lijie Yang
8. Qing Lei

In Endocrinology, Diabetes and Metabolism

1. Jun Liu
2. Qiuhong Ke
3. Hossein Rahmani
4. Zehua Sun

Citations in the corpus

• A Comprehensive Survey of Vision-Based Human Action Recognition Methods (2019, background): “[76] also analyzed the spatiotemporal depth structure in the front, side, and upper directions.”
• Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Some methods [259], [260] for HAR from depth map videos were developed based on projected depth maps (i.”